A Time Series
Moving Average is similar to a Simple Moving Average, except that values are
derived from linear regression forecast values instead of raw values. A Moving
Average is most often used to average values for a smoother representation of
the underlying price or indicator. The time series moving average is calculated
using linear regression techniques. Rather than plotting a straight linear
regression line, a time series moving average plots the last point of the line.
The Moving Average (Time Series) function returns the moving average of a field
over a given period of time based on linear regression.

The time series
moving average is calculated by fitting a linear regression line over the
values for the given period, and then determining the current value for that
line. A linear regression line is a straight line, which is as close to all of
the given values as possible.

Moving averages
are useful for smoothing noisy raw data. By looking at the moving average of
the price, a more general picture of the underlying trends can be seen. Since
moving averages can be used to see trends, they can also be used to see whether
data is bucking the trend. Entry/exit systems often compare data to a moving
average to determine whether it is supporting a trend or starting a new one.

Use of this Site is subject to express Terms of Use. By using this Site, you signify that you agree to be bound by these
Terms of Use.Copyright 2013 vogaz. All rights reserved. Reproduction of News, Articles, Photos, Videos or any other content in whole or in part in any form or medium without express written permission of
www.vogaz.com is prohibited.